Adaptation and user expertise modelling in AthosMail



This article describes the User Model component of AthosMail, a speech-based interactive e-mail application developed in the context of the EU project DUMAS. The focus is on the system’s adaptive capabilities and user expertise modelling, exemplified through the User Model parameters dealing with initiative and explicitness of the system responses. The purpose of the conducted research was to investigate how the users could interact with a system in a more natural way, and the two aspects that mainly influence the system’s interaction capabilities, and thus the naturalness of the dialogue as a whole, are considered to be the dialogue control and the amount of information provided to the user. The User Model produces recommendations of the system’s appropriate reaction depending on the user’s observed competence level, monitored and computed on the basis of the user’s interaction with the system. The article also discusses methods for the evaluation of adaptive user models and presents results from the AthosMail evaluation.


User modelling Adaptation Evaluation Speech-based human computer interaction Mobile e-mail applications 


  1. 1.
    Allwood J, Traum D, Jokinen K (2001) Cooperation, dialogue and ethics. Int J Hum-Comput Stud 53:871–914CrossRefGoogle Scholar
  2. 2.
    Benyon D, Murray D (1993) Developing adaptive systems to fit individual aptitudes. Intell User Interfaces, 115–121Google Scholar
  3. 3.
    Berglund A, Johansson P (2004) Using speech and dialogue for interactive TV navigation. Univ Access Inf Soc 3(3–4):224–238CrossRefGoogle Scholar
  4. 4.
    Brusilovsky P, Maybury MT (2002) From adaptive hypermedia to the adaptive Web. Commun ACM 45(5):30–33CrossRefGoogle Scholar
  5. 5.
    Chesnais P, Mucklo M, Sheena JA (1995) The Fishwrap personalized news system, IEEE Second International Workshop on Community Networking Integrating Multimedia Services to the Home. Princeton, NJ, USACrossRefGoogle Scholar
  6. 6.
    Chin D (1989) KNOME: modeling what the user knows in UC. In: Kobsa A, Wahlster W (eds) User Modeling in Dialogue Systems. Springer, Berlin Heidelberg New York, pp 74–107Google Scholar
  7. 7.
    Chu-Carroll J (2000) MIMIC: an adaptive mixed initiative spoken dialogue system for information queries. In: Proceedings of ANLP 6, pp 97–104Google Scholar
  8. 8.
    Chu-Carroll J, Nickerson JS (2000) Evaluating automatic dialogue strategy adaptation for a spoken dialogue system. In: Proceedings of NAACL 1, pp 202–209Google Scholar
  9. 9.
    Dai W, Cohen R (2003) Dynamic personalized TV recommendation system. Proceedings of the UM 2003 Workshop on Personalization in Future TV, Pittsburgh, US, pp 12–21Google Scholar
  10. 10.
    Danieli M, Gerbino E (1995) Metrics for evaluating dialogue strategies in a spoken language system. In: Proceedings of the AAAI Spring Symposium on Empirical Methods in Discourse Interpretation and Generation, pp 34–39Google Scholar
  11. 11.
    Dreyfus HL, Dreyfus SE (1986) Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. The Free Press, New YorkGoogle Scholar
  12. 12.
    Dybkjær L, Bernsen NO, Dybkjær H (1997) Designing co-operativity in spoken human-machine dialogues. In: Varghese K, Pfleger S (eds) Human Comfort and Security of Information Systems. Advanced Interfaces for the Information Society. Springer, Berlin Heidelberg New York, pp 104–124Google Scholar
  13. 13.
    Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):51–60CrossRefGoogle Scholar
  14. 14.
    Grice HP (1975) Logic and Conversation. In: Cole P, Morgan J (eds) Syntax and Semantics. vol 3. Academic, New York, pp 43–58Google Scholar
  15. 15.
    Höök K (2000) Steps to take before intelligent user interfaces become real. J Interact Computers 12(4):409–426CrossRefGoogle Scholar
  16. 16.
    Jameson A, Schwarzkopf E (2002) Pros and Cons of Controllability:An Empirical Study. Lect Notes Comput Sci 2347:193–202CrossRefGoogle Scholar
  17. 17.
    Jokinen K (1996) Goal formulation based on communicative principles. In: Proceedings of the 16th International Conference on Computational Linguistics, Copenhagen, Denmark, pp 598–603Google Scholar
  18. 18.
    Jokinen K (2003) Natural interaction in spoken dialogue systems. In: Stephanidis C, Jacko J (eds) Human–Computer Interaction: Theory and Practice (Part II), vol 4. Lawrence Erlbaum Associates, Mahwah, pp 730–734Google Scholar
  19. 19.
    Jokinen K, Gambäck B (2004) DUMAS—adaptation and robust information processing for mobile speech interfaces. In: Proceedings of the First Baltic Conference “Human Language Technologies – The Baltic Perspective”, Riga, Latvia, pp 115–120Google Scholar
  20. 20.
    Jokinen K, Kanto K (2004) User expertise modelling and adaptivity in a speech-based e-mail system. In: Proceedings of the ACL-04, Barcelona, Spain, pp 88–95Google Scholar
  21. 21.
    Jokinen K, Kanto K, Kerminen A, Rissanen J (2004) Evaluation of adaptivity and user expertise in a speech based e-mail system. In: Proceedings of the COLING Satellite Workshop Robust and Adaptive Information Processing for Mobile Speech Interfaces, Geneva, Switzerland, pp 44–52Google Scholar
  22. 22.
    Jokinen K, Kanto K, Rissanen J (2004) Adaptive user modelling in Athosmail. In: Stary C, Stephanidis C (eds) UI4All, Lecture Notes in Computer Science 3196, pp 149–158 Springer, Berlin Heidelberg New York. Also available online:
  23. 23.
    Jokinen K, Rissanen J, Keränen H, Kanto K (2002) Learning interaction patterns for adaptive user interfaces. In: Adjunct Proceedings of the Seventh ERCIM Workshop User Interfaces for All, Paris, France, pp 53–58Google Scholar
  24. 24.
    Jokinen K, Wilcock G (2003) Adaptivity and response generation in a spoken dialogue system. In: van Kuppevelt J, Smith RW (eds) Current and New Directions in Discourse and Dialogue. Kluwer Academic, Dordrecht, pp 213–234Google Scholar
  25. 25.
    Kamm C, Litman L, Walker M (1998) From novice to expert: the effect of tutorials on user expertise with spoken dialogue systems. In: Proceedings of the Fifth International Conference on Spoken Language Processing (ICSLP98), pp 1211–1212Google Scholar
  26. 26.
    Kanerva P, Kristoferson J, Holst A (2000) Random indexing of text samples for latent semantic analysis. In: Proceedings of the 22nd Annual Conference of the Cognitive Science Society. Lawrence Erlbaum Associates, Mahwah, p 1036Google Scholar
  27. 27.
    Kanto K, Cheadle M, Gambäck B, Hansen P, Jokinen K, Keränen H, Rissanen J (2003) Multi-session group scenarios for speech interface design. In: Stephanidis C, Jacko J (eds) Human-Computer Interaction: Theory and Practice (Part II), vol 2. Lawrence Erlbaum Associates, Mahwah, pp 676–680Google Scholar
  28. 28.
    Karlgren J, Sahlgren J (2001) From Words to Understanding. In: Uesaka Y, Kanerva P, Asoh H (eds) 2001. Foundations of Real-World Intelligence. CSLI Publications, Stanford, pp 294–308Google Scholar
  29. 29.
    Kay J (2001) Learner control. User Model User-Adapted Interact 11:111–127CrossRefMATHGoogle Scholar
  30. 30.
    Krahmer E, Swerts M, Theune M, Weegels M (1999) Problem spotting in human–machine interaction. In: Proceedings of Eurospeech ’99, vol 3. Budapest, Hungary, pp 1423–1426Google Scholar
  31. 31.
    Litman DJ, Pan S (2002) Designing and evaluating an adaptive spoken dialogue system. User Model User-Adapted Interact 12(2/3):111–137CrossRefMATHGoogle Scholar
  32. 32.
    Malone T, Grant K, Turbak F, Brobst S, Cohen M (1987) Intelligent information-sharing systems. Commun ACM 30(5):390–402CrossRefGoogle Scholar
  33. 33.
    Moukas A, Maes P (1998) Amalthaea: an evolving multi-agent information filtering and discovery system for the WWW. Autonomous Agents Multi-Agent Syst 1:(1):59–88CrossRefGoogle Scholar
  34. 34.
    Möller S (2002) A new taxonomy for the quality of telephone services based on spoken dialogue systems. In: Jokinen K, McRoy S (eds) Proceedings of the Third SIGDial Workshop, Philadelphia, US, pp 142–153Google Scholar
  35. 35.
    Oppermann R (1994) Adaptively supported adaptability. Int J Hum Comput Stud 40:455–472CrossRefGoogle Scholar
  36. 36.
    Paramythis A, Totter A, Stephanidis C (2001) A modular approach to the evaluation of adaptive user interfaces. In: Proceedings of the eight international conference on User Modeling, Sonthofen, Germany, pp 9–24Google Scholar
  37. 37.
    Paris C (1988) Tailoring Descriptions to a User’s Level of Expertise. J Comput Linguist 14(3):64–78Google Scholar
  38. 38.
    Smith RW (1993) Effective spoken natural language dialog requires variable initiative behavior: an empirical study. In: Proceedings of the AAAI Fall Symposium on Human–Computer Collaboration: Reconciling Theory, Synthesizing PracticeGoogle Scholar
  39. 39.
    Smith RW, Hipp DR (1994) Spoken Natural Language Dialog Systems—A Practical Approach. Oxford University Press, OxfordGoogle Scholar
  40. 40.
    Turunen M, Salonen E-P, Hartikainen M, Hakulinen J, Black WJ, Ramsay A, Funk A, Conroy A, Thompson P, Stairmand M, Jokinen K, Rissanen J, Kanto K, Kerminen A, Gambäck B, Cheadle M, Olsson F, Sahlgren M (2004) AthosMail—a multilingual adaptive spoken dialogue system for e-mail domain. In: Proceedings of the COLING Workshop Robust and Adaptive Information Processing for Mobile Speech Interfaces, Geneva, Switzerland, pp 78–86Google Scholar
  41. 41.
    Walker MA, Langkilde I, Wright J, Gorin A, Litman DJ (2000) Learning to predict problematic situations in a spoken dialogue system: experiments with How May I Help You? In: Proceedings of NAACL’00, Seattle, US, pp 210–217Google Scholar
  42. 42.
    Walker M, Litman DJ, Kamn CA, Abella A (1998) Evaluating spoken dialogue agents with PARADISE: two case studies. Comput Speech Lang 12(3)CrossRefGoogle Scholar
  43. 43.
    Yankelovich N (1996) How do users know what to say? Interactions 3(6):32–43CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.University of HelsinkiHelsinkiFinland

Personalised recommendations